As advances in VLSI and MEMS technologies have boosted the development of integrated microsystems that combine mobility, computing, communication, and sensing on a single platform, military and civilian operations develop the capability to exploit large numbers of interconnected agents such as low-cost and small-in-size autonomous vehicles and microsensors. Such large-scale multiagent systems will support operations ranging from environment monitoring and military surveillance, to guidance, navigation, and control of autonomous underwater, ground, aerial, and space vehicles.
Yet, current distributed control methods lack information exchange infrastructures to enable spatially evolving multiagent formations. This is due to the fact that these current methods are designed based on information exchange rules for a network having a single layer, which leads to multiagent formations with fixed, non-evolving spatial properties. For situations where capable agents have to control the resulting formation through these methods, they can only do so if such vehicles have global information exchange ability. For example, global information may include scaling factor and rotation angle to control the density and orientation of the formation that is communicated to all agents in the network from capable agents. Therefore, it requires capable agents to globally communicate and/or broadcast global information to every single agent in the network. However, such global information exchange is not practical for cases involving large numbers of agents and low-bandwidth peer-to-peer communications.
Studies on multiplex information networks have recently emerged in physics and network science literature. The studies consider system-theoretic characteristics of network dynamics with multiple layers subject to intra-layer and interlayer information exchange. There also exist studies on multiplex networks that do not consider system-theoretic characteristics. However, these studies mainly consider cases where all layers perform simple consensus algorithms and analyze the convergence of the overall multiagent systems in the presence of not only intra-layer but also interlayer information exchange, and hence, they do not deal with controlling spatial properties of multiagent formations. Moreover, there are recent studies on networks of networks. However, these studies deal with large-scale systems formed from smaller factor networks via graph Cartesian products and are not related.
Spatial multiagent formation control and formation density control in particular have been considered. However, formation design parameters were known globally by all agents, and global knowledge of the complete network at the analysis stage was assumed. Such an approach may not be practical in the presence of large numbers of agents and low-bandwidth peer-to-peer communications. Moreover, from a data security point of view, a multiagent system with all agents sharing some global information about an operation of interest may not be desired.